TY - JOUR
T1 - Bayesian inference of drag parameters using AXBT data from typhoon fanapi
AU - Sraj, Ihab
AU - Iskandarani, Mohamed
AU - Srinivasan, Ashwanth
AU - Thacker, W. Carlisle
AU - Winokur, Justin
AU - Alexanderian, Alen
AU - Lee, Chia Ying
AU - Chen, Shuyi S.
AU - Knio, Omar M.
PY - 2013
Y1 - 2013
N2 - The authors introduce a three-parameter characterization of the wind speed dependence of the drag coefficient and apply a Bayesian formalism to infer values for these parameters from airborne expendable bathythermograph (AXBT) temperature data obtained during Typhoon Fanapi. One parameter is a multiplicative factor that amplifies or attenuates the drag coefficient for all wind speeds, the second is the maximum wind speed at which drag coefficient saturation occurs, and the third is the drag coefficient's rate of change with increasing wind speed after saturation. Bayesian inference provides optimal estimates of the parameters as well as a non-Gaussian probability distribution characterizing the uncertainty of these estimates. The efficiency of this approach stems from the use of adaptive polynomial expansions to build an inexpensive surrogate for the high-resolution numerical model that couples simulated winds to the oceanic temperature data, dramatically reducing the computational burden of the Markov chain Monte Carlo sampling. These results indicate that the most likely values for the drag coefficient saturation and the corresponding wind speed are about 2.3 3 10-3 and 34ms-1, respectively; the data were not informative regarding the drag coefficient behavior at higher wind speeds.
AB - The authors introduce a three-parameter characterization of the wind speed dependence of the drag coefficient and apply a Bayesian formalism to infer values for these parameters from airborne expendable bathythermograph (AXBT) temperature data obtained during Typhoon Fanapi. One parameter is a multiplicative factor that amplifies or attenuates the drag coefficient for all wind speeds, the second is the maximum wind speed at which drag coefficient saturation occurs, and the third is the drag coefficient's rate of change with increasing wind speed after saturation. Bayesian inference provides optimal estimates of the parameters as well as a non-Gaussian probability distribution characterizing the uncertainty of these estimates. The efficiency of this approach stems from the use of adaptive polynomial expansions to build an inexpensive surrogate for the high-resolution numerical model that couples simulated winds to the oceanic temperature data, dramatically reducing the computational burden of the Markov chain Monte Carlo sampling. These results indicate that the most likely values for the drag coefficient saturation and the corresponding wind speed are about 2.3 3 10-3 and 34ms-1, respectively; the data were not informative regarding the drag coefficient behavior at higher wind speeds.
UR - http://www.scopus.com/inward/record.url?scp=84880711478&partnerID=8YFLogxK
U2 - 10.1175/MWR-D-12-00228.1
DO - 10.1175/MWR-D-12-00228.1
M3 - Article
AN - SCOPUS:84880711478
SN - 0027-0644
VL - 141
SP - 2347
EP - 2367
JO - Monthly Weather Review
JF - Monthly Weather Review
IS - 7
ER -